Consumers who are interested in identifying or discovering items for purchase using computer-based systems or methods may generally do so in one of two ways. First, consumers may search for items or categories of items by entering a keyword into a search engine, which then returns information relating to one or more items or categories of items for review by the user. Second, consumers may also browse through broad lists of items or categories of items in an electronic catalog, and may select a keyword pertaining to an item or a category of items for review and consideration.
Searching and browsing for items online may have limited effectiveness, however, for several reasons. First, the quality of the information returned through searching or browsing is only as good as the keyword entered or selected by a user. In the event that the information returned through searching or browsing is not satisfactory, a user must start over again, and repeat the process using new keywords or categories. Next, while the relationship between the keyword searched or browsed and the information returned may be readily apparent, how that information relates to other items or keywords cannot be determined until subsequent searches or selections are performed. Finally, systems and methods for searching and browsing are typically unable to express degrees of association between a keyword entered or selected by a user and other keywords. Therefore, identifying items for purchase online frequently depends on a consumer's willingness and ability to repeat the searching and browsing process through multiple iterations, and can be inefficient.
Systems and methods for determining and presenting relationships between a selected item and other related items are known to those of skill in the art. For example, U.S. Pat. No. 6,266,649 B1 to Linden et al., which is incorporated by reference in its entirety herein, is directed to computer-implemented systems and methods for generating personalized recommendations of items based on the collective interests of a community of users. Linden describes systems and methods for mapping items to other similar items on a periodic basis by identifying correlations between the known interests of users of particular items, and presenting a list of recommended items to a user who is interested in purchasing an item.
However, the systems and methods described in Linden are one-dimensional, in that searching for one item results in a list of related items and nothing more, and the methods described in Linden must be repeated in order to return multiple sets of results. Moreover, the related items provided by the systems and methods described in Linden are presented in a list form only, and Linden does not enable a user to view a degree of association between the originally entered item and each of the related items in the list. The systems and methods discussed in Linden also do not inform a user whether any relationships between the related items exist.